Solution: System Integration Industry: Telecoms
Better Business Data Usage Through System Integration
Solita is a fast-growing community of more than 1,500 experts from Finland, Sweden, Denmark, Estonia, Belgium and Germany. For over 25 years, they have offered a unique portfolio of services, including strategic consulting, service design, software development, artificial inteliigence and analytics, cloud services and integration services.
The organization approached Unity Group with a data management project for DNA, a Finnish telecommunications company. The goal was to enhance the company’s ability to leverage its data in both the operational and strategic levels.
- DNA wanted to simplify their IT architecture and replace outdated solutions with new alternatives.
- It was also important to automate processes, alongside faster and wider access to data, in order to create reports on a larger scale.
- The range of services needed to be expanded to enable the selling of new reports.
- Different data models with similar purposes needed to be integrated. For example, there were several systems providing invoice data.
- All data transfers across multiple sources needed to be fast and secure.
- The integration architecture used consists of 4 environments: three test environments (including User Acceptance Tests) and a production environment. These include both servers forming clusters in AWS and on-premise options. A similar architecture was developed for Apache Kafka .
- The implementation was created in Kotlin, the application framework is Apache Camel and Spring Boot, while Gradle and Jib are used to create the docker image. The architecture is deployed to servers through the use of Jenkins jobs. The applications have been dockerized and run like microservices.
- APIs are built mostly on data from databases, such as PostgreSQL, SQL Server, Snowflake or Elasticsearch. They make information available to other systems. Users can generate reports on finances, their subscriptions or information on purchased equipment or service packages.
- By aggregating invoice data from many structurally different tables into an index in Elasticsearch, the amount of data available to the user has increased from 3 months to 2 years.
- We also implemented several applications to transfer data as quickly as possible between Kafka and/or S3, as well as applications to ensure the integrity of key data in closely related systems. Thanks to the solutions used, we were able to achieve near real-time data availability and updates.
- We designed and implemented the use of Kafka Connect to be able to read data directly from Kafka without the need to create an implementation in Camel. Data read in this way can be sent to Snowflake.
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